# Audio Classification with PANNS
*Author: Jael Gu*
## Desription
The audio classification operator classify the given audio data with 527 labels from the large-scale [AudioSet dataset ](https://research.google.com/audioset/ ).
The pre-trained model used here is from the paper **PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition** ([paper link](https://arxiv.org/abs/1912.10211)).
```python
import numpy as np
from towhee import ops
audio_classifier = ops.audio_classification.panns()
# Path or url as input
tags, audio_embedding = audio_classifier("/audio/path/or/url/")
# Audio data as input
audio_data = np.zeros((2, 441344))
sample_rate = 44100
tags, audio_embedding = audio_classifier(audio_data, sample_rate)
```
## Factory Constructor
Create the operator via the following factory method
***ops.audio_classification.panns()***
## Interface
Given an audio (file path, link, or waveform),
the audio classification operator generates a list of labels
and a vector in numpy.ndarray.
**Parameters:**
None.
**Returns**: *numpy.ndarray*
labels [(tag, score)], audio embedding in shape (2048,).
## Code Example
Generate embeddings for the audio "test.wav".
*Write the pipeline in simplified style* :
```python
from towhee import dc
dc.glob('test.wav')
.audio_classification.panns()
.show()
```
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
from towhee import dc
dc.glob['path']('test.wav')
.audio_classification.panns['path', 'vecs']()
.select('vecs')
.show()
```
